I want to have an interactive plot in jupyter (4.0.6) notebook using matplotlib (1.5.1). The thing is that the static plot is created with a function that has four variables, two of them are constants, two of them are keyword arguments, and I want to interactively change the keyword arguments.
Is this possible, and if yes, how?
The conceptual code below shows the function that generates a plot make_figure(...) and the command to generate an interactive plot.
If I change the keyword arguments to variables, then I get the error message "interact() takes from 0 to 1 positional arguments but 3 were given"
conceptual code:
def make_figure(const_1, const_2, var_1=0.4, var_2=0.8):
b = calc_b(var_1, var_2)
c = calc_c(b, const_1, const_2)
fig, ax = plt.subplots()
N, bins, patches = ax.hist(c)
interact(make_figure,
const_1,
const_2,
var_1=(0.2, 0.4, 0.05),
var_2=(0.75, 0.95, 0.05))
addition 20160325: code example
I am trying to create a histogram for marks for a class, dependent on the percentage necessary to achieve a 1.0 and a 4.0, respectively.
# setup some marks
ids_perc = np.random.random(33)
print("number of entered marks: ", ids_perc.shape)
the main code for the histogram; main function: get_marks
# define possible marks
marks = np.array([1.0,
1.3,
1.7,
2.0,
2.3,
2.7,
3.0,
3.3,
3.7,
4.0,
5.0])
marks_possible = marks[::-1]
def get_perc_necessary(min_perc_one,
min_perc_four,
n_marks):
"""
calculates an equally spaced array for percentage necessary to get a mark
"""
delta = (min_perc_one - min_perc_four)/(n_marks-2-1)
perc_necessary_raw = np.linspace(start=min_perc_four,
stop=min_perc_one,
num=n_marks-1)
perc_necessary = np.append([0.0], np.round(perc_necessary_raw, decimals=2))
return perc_necessary
def assign_marks(n_students,
perc_necessary,
achieved_perc,
marks_real):
"""
get the mark for each student (with a certain achieved percentage)
"""
final_marks = np.empty(n_students)
for cur_i in range(n_students):
idx = np.argmax(np.argwhere(perc_necessary <= achieved_perc[cur_i]))
final_marks[cur_i] = marks_real[idx]
return final_marks
def get_marks(achieved_perc = ids_perc,
marks_real = marks_possible,
min_perc_four = 0.15,
min_perc_one = 0.85):
n_marks = marks.shape[0]
# print("n_marks: ", n_marks)
n_students = achieved_perc.shape[0]
# print("n_students: ", n_students)
# -----------------------------
# linear step between each mark
perc_necessary = get_perc_necessary(min_perc_one,
min_perc_four,
n_marks)
# test query: there need to be as many percentages as marks
if perc_necessary.shape[0] != marks_real.shape[0]:
print("the number of marks has to be equal the number of boundaries")
raise Exception
# ------------
# assign marks
final_marks = assign_marks(n_students,
perc_necessary,
achieved_perc,
marks_real)
# ------------
# create table
fig, ax = plt.subplots()
N, bins, patches = ax.hist(final_marks,
align='mid',
bins=np.append(marks,6.)) # bins=marks
ax.xaxis.set_major_formatter(FormatStrFormatter('%0.1f'))
bin_centers = 0.5 * np.diff(bins) + bins[:-1]
ax.set_xticks(bin_centers)
ax.set_xticklabels( marks )
ax.set_xlabel("mark")
ax.set_ylabel("number of marks")
ax.set_ylim(0.0, 6.0)
plt.grid(True)
Now, when I try to setup interact doing this
interact(get_marks,
min_perc_four=(0.2, 0.4, 0.05),
min_perc_one=(0.75, 0.95, 0.05));
I get the error
ValueError: array([ 0.22366653, 0.74206953, 0.47501716, 0.56536227, 0.54792759,
0.60288287, 0.68548973, 0.576935 , 0.84582243, 0.40709693,
0.78600622, 0.2692508 , 0.62524819, 0.62204851, 0.5421716 ,
0.71836192, 0.97194698, 0.4054752 , 0.2185643 , 0.11786751,
0.57947848, 0.88659768, 0.38803576, 0.66617254, 0.77663263,
0.94364543, 0.23021637, 0.30899724, 0.08695842, 0.50296694,
0.8164095 , 0.77892531, 0.5542163 ]) cannot be transformed to a Widget
Why is this error looking at the variable ids_perc?
You need to assign your variables explicitly in interact(). For example, like this:
const_1 = 1
interact(make_figure,
const_1=const_1,
const_2=2,
var_1=(0.2, 0.4, 0.05),
var_2=(0.75, 0.95, 0.05))
Or (if possible) change the signature of make_figure to make those variables into keyword arguments, so that you can avoid passing them explicitly:
def make_figure(const_1=1, const_2=2, var_1=0.4, var_2=0.8):
....
interact(make_figure,
var_1=(0.2, 0.4, 0.05),
var_2=(0.75, 0.95, 0.05))
Here is MCWE that you can try:
def calc_b(v1, v2):
return v1 + v2
def calc_c(v1, v2, v3):
return [v1, v2, v3]
def make_figure(const_1=1, const_2=2, var_1=0.4, var_2=0.8):
b = calc_b(var_1, var_2)
c = calc_c(b, const_1, const_2)
fig, ax = plt.subplots()
N, bins, patches = ax.hist(c)
interact(make_figure,
var_1=(0.2, 0.4, 0.05),
var_2=(0.75, 0.95, 0.05));
This runs without any errors.
On your addition 20160325:
Every parameter you pass to interact will have to be representable by either one of (simplifying it somewhat):
tuples, that represent (min, max) and scalar numbers)You are passing (implicitly by defining in your get_marks two parameters as np.arrays).
So interact doesn't know how to represent that on a slider, hense the error.
You have at least two options:
1) to change the signature of the get_marks so that it takes parameters that interact will undetstand (see bullet list above)
2) make another wrapper function that will take parameters that interact undetstands, but will call get_marks after converting those parameters to whatever get_marks needs.
So just one extra step and you're done. ;-)
UPDATE:
Here is your code with wrapper that works for me.
Note that get_marks_interact does not need to take all the params of get_marks and I don't pass lists as interact will have a problem with them (list should represent either a list of strings (for Dropdown Widgets) or list/tuple of [min, max] values (for slider)).
def get_marks(min_perc_four = 0.15,
min_perc_one = 0.85,
marks=marks_possible,
ach_per=ids_perc):
marks_real = marks # [0]
achieved_perc = ach_per # [0]
n_marks = marks_real.shape[0]
print("n_marks: ", n_marks)
n_students = achieved_perc.shape[0]
print("n_students: ", n_students)
# -----------------------------
# linear step between each mark
perc_necessary = get_perc_necessary(min_perc_one,
min_perc_four,
n_marks)
# test query: there need to be as many percentages as marks
if perc_necessary.shape[0] != marks_real.shape[0]:
print("the number of marks has to be equal the number of boundaries")
raise Exception
# ------------
# assign marks
final_marks = assign_marks(n_students,
perc_necessary,
achieved_perc,
marks_real)
# ------------
# create table
fig, ax = plt.subplots()
N, bins, patches = ax.hist(final_marks,
align='mid',
bins=np.sort(np.append(marks, 6.))) # bins=marks
ax.xaxis.set_major_formatter(FormatStrFormatter('%0.1f'))
bin_centers = 0.5 * np.diff(bins) + bins[:-1]
ax.set_xticks(bin_centers)
ax.set_xticklabels( marks )
ax.set_xlabel("mark")
ax.set_ylabel("number of marks")
ax.set_ylim(0.0, 6.0)
plt.grid(True)
def get_marks_interact(min_perc_four = 0.15,
min_perc_one = 0.85,):
return get_marks(min_perc_four, min_perc_one)
interact(get_marks_wrapper,
min_perc_four=(0.2, 0.4, 0.05),
min_perc_one=(0.75, 0.95, 0.05));